Failure Mode and Effects Analysis (FMEA) integrating quality indicators for risk assessment of the total testing process in human papillomavirus genotyping testing: a proactive risk analysis model for molecular diagnostics
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Tingting Li
,
Yuting He
Abstract
Objectives
To develop a proactive risk assessment model for human papillomavirus (HPV) genotyping testing by integrating Failure Mode and Effects Analysis (FMEA) with quality indicators (QIs), ensuring compliance with ISO 15189:2022 and improving diagnostic accuracy.
Methods
A multidisciplinary team designed and performed detailed FMEA across pre-analytical, analytical, and post-analytical phases of HPV genotyping testing. To improve objectivity, we integrated Sigma metrics into the FMEA framework through a molecular diagnostics-specific model of QIs (MQI). The FMEA model systematically identified testing phases, potential failure modes, their effects, root causes, and existing controls. Risk was quantified using Severity, Occurrence (from 1-year QI data), and Detection scores (1–5 scale). Risk Priority Numbers (RPNs) were calculated (Severity × Occurrence × Detection) to prioritize failure modes, with mandatory interventions implemented for high-risk items (RPN≥40).
Results
Five high-risk failure modes (e.g., sample misidentification, data analysis errors) were identified and successfully mitigated to acceptable levels (RPN<40) through process optimization and standardization, achieving RPN reductions of 20–80 %. We established a molecular diagnostics-specific MQI, comprising 14 pre-analytical, 25 analytical, and three post-analytical phase QIs. QI-based risk assessment of 35 evaluable QIs for HPV genotyping testing revealed one high-risk QI (“Incorrect results due to information system failures”) and three medium-risk QIs, all of which were addressed through corrective actions.
Conclusions
This study developed an integrated FMEA-QI model for HPV genotyping testing, establishing both a traditional FMEA framework and a molecular diagnostics-specific MQI. The combined approach improves risk assessment objectivity and enables multidimensional analysis compared to conventional methods.
Funding source: Guangdong Basic and Applied Basic Research Foundation
Award Identifier / Grant number: 2021A1515220163
Funding source: the Project of Administration of Traditional Chinese Medicine of Guangdong Province of China
Award Identifier / Grant number: 20251154
Funding source: Guangdong Provincial Medical Science and Technology Research Fund Project
Award Identifier / Grant number: B2023223
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: Tingting Li led the study design and manuscript preparation. Yuanhao Chen contributed to the investigation of clinical applications for human papillomavirus (HPV) genotyping testing, co-developed the Failure Mode and Effects Analysis (FMEA) model, and organized questionnaire data. Shunwang Cao, Yi Wang, Cheng Zhang, and Hongmei Wang collaboratively established the FMEA framework, conducted risk assessments, and implemented risk control measures. Yuting He critically reviewed the manuscript for intellectual content and accuracy. Peifeng Ke oversaw the entire project, formulated risk acceptability criteria, and ensured methodological rigor. All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: This study was supported by grants from the Project of Administration of Traditional Chinese Medicine of Guangdong Province of China (grant no. 20251154), Guangdong Basic and Applied Basic Research Foundation (grant no. 2021A1515220163) and Guangdong Provincial Medical Science and Technology Research Fund Project (B2023223).
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Data availability: Not applicable.
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Supplementary Material
This article contains supplementary material (https://doi.org/10.1515/cclm-2025-0598).
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